{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_df = pd.read_csv('covid-19/countries-aggregated.csv')\n",
    "\n",
    "ww_df = pd.read_csv('covid-19/worldwide-aggregated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24816, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Country  Confirmed  Recovered  Deaths\n",
       "0  2020-01-22  Afghanistan          0          0       0\n",
       "1  2020-01-22      Albania          0          0       0\n",
       "2  2020-01-22      Algeria          0          0       0\n",
       "3  2020-01-22      Andorra          0          0       0\n",
       "4  2020-01-22       Angola          0          0       0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ca_df.shape)\n",
    "\n",
    "ca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(132, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Increase rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>555</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>654</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>17.837838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>941</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>43.883792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1434</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>52.391073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2118</td>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "      <td>47.698745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Confirmed  Recovered  Deaths  Increase rate\n",
       "0  2020-01-22        555         28      17            NaN\n",
       "1  2020-01-23        654         30      18      17.837838\n",
       "2  2020-01-24        941         36      26      43.883792\n",
       "3  2020-01-25       1434         39      42      52.391073\n",
       "4  2020-01-26       2118         52      56      47.698745"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ww_df.shape)\n",
    "\n",
    "ww_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24816 entries, 0 to 24815\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Date       24816 non-null  object\n",
      " 1   Country    24816 non-null  object\n",
      " 2   Confirmed  24816 non-null  int64 \n",
      " 3   Recovered  24816 non-null  int64 \n",
      " 4   Deaths     24816 non-null  int64 \n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 969.5+ KB\n"
     ]
    }
   ],
   "source": [
    "ca_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132 entries, 0 to 131\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Date           132 non-null    object \n",
      " 1   Confirmed      132 non-null    int64  \n",
      " 2   Recovered      132 non-null    int64  \n",
      " 3   Deaths         132 non-null    int64  \n",
      " 4   Increase rate  131 non-null    float64\n",
      "dtypes: float64(1), int64(3), object(1)\n",
      "memory usage: 5.3+ KB\n"
     ]
    }
   ],
   "source": [
    "ww_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132 entries, 0 to 131\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Date           132 non-null    datetime64[ns]\n",
      " 1   Confirmed      132 non-null    int64         \n",
      " 2   Recovered      132 non-null    int64         \n",
      " 3   Deaths         132 non-null    int64         \n",
      " 4   Increase rate  131 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(3)\n",
      "memory usage: 5.3 KB\n"
     ]
    }
   ],
   "source": [
    "ww_df['Date'] = pd.to_datetime(ww_df['Date'])\n",
    "\n",
    "ww_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "sns.lineplot(x = 'Date', y = 'Confirmed', data = ww_df)\n",
    "\n",
    "plt.xticks(rotation = 45)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24816 entries, 0 to 24815\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       24816 non-null  datetime64[ns]\n",
      " 1   Country    24816 non-null  object        \n",
      " 2   Confirmed  24816 non-null  int64         \n",
      " 3   Recovered  24816 non-null  int64         \n",
      " 4   Deaths     24816 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(3), object(1)\n",
      "memory usage: 969.5+ KB\n"
     ]
    }
   ],
   "source": [
    "ca_df['Date'] = pd.to_datetime(ca_df['Date'])\n",
    "\n",
    "ca_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Sahara</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Date  Confirmed  Recovered  Deaths\n",
       "Country                                                    \n",
       "Afghanistan        2020-01-22          0          0       0\n",
       "Albania            2020-01-22          0          0       0\n",
       "Algeria            2020-01-22          0          0       0\n",
       "Andorra            2020-01-22          0          0       0\n",
       "Angola             2020-01-22          0          0       0\n",
       "...                       ...        ...        ...     ...\n",
       "West Bank and Gaza 2020-01-22          0          0       0\n",
       "Western Sahara     2020-01-22          0          0       0\n",
       "Yemen              2020-01-22          0          0       0\n",
       "Zambia             2020-01-22          0          0       0\n",
       "Zimbabwe           2020-01-22          0          0       0\n",
       "\n",
       "[188 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_ca_df = ca_df.groupby('Country')\n",
    "\n",
    "group_ca_df.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi', 'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada', 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'Mauritania', 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Norway', 'Oman', 'Pakistan', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia', 'Saint Vincent and the Grenadines', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia', 'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan*', 'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay', 'Uzbekistan', 'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Western Sahara', 'Yemen', 'Zambia', 'Zimbabwe'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_ca_df.groups.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 14))\n",
    "\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "\n",
    "plt.subplot(311)\n",
    "sns.lineplot(x = 'Date', y = 'Confirmed', data = group_ca_df.get_group('India'))\n",
    "plt.title('India')\n",
    "\n",
    "plt.subplot(312)\n",
    "sns.lineplot(x = 'Date', y = 'Confirmed', data = group_ca_df.get_group('China'))\n",
    "plt.title('China')\n",
    "\n",
    "plt.subplot(313)\n",
    "sns.lineplot(x = 'Date', y = 'Confirmed', data = group_ca_df.get_group('US'))\n",
    "plt.title('US')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>China</td>\n",
       "      <td>548</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>China</td>\n",
       "      <td>643</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Country  Confirmed  Recovered  Deaths\n",
       "36  2020-01-22   China        548         28      17\n",
       "79  2020-01-22   India          0          0       0\n",
       "174 2020-01-22      US          1          0       0\n",
       "224 2020-01-23   China        643         30      18\n",
       "267 2020-01-23   India          0          0       0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "temp_df = ca_df.loc[(ca_df.Country=='China') | (ca_df.Country=='India') | (ca_df.Country=='US'), : ]\n",
    "\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 396 entries, 36 to 24802\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       396 non-null    datetime64[ns]\n",
      " 1   Country    396 non-null    object        \n",
      " 2   Confirmed  396 non-null    int64         \n",
      " 3   Recovered  396 non-null    int64         \n",
      " 4   Deaths     396 non-null    int64         \n",
      "dtypes: datetime64[ns](1), int64(3), object(1)\n",
      "memory usage: 18.6+ KB\n"
     ]
    }
   ],
   "source": [
    "temp_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "sns.lineplot(x = 'Date', y = 'Confirmed', hue='Country', data = temp_df)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
